Does theory contribute to forecasting accuracy? We use event data to show that a parsimonious model grounded in prominent theories of conflict escalation can forecast civil war onset with high accuracy and over shorter temporal windows than has generally been possible. Our forecasting model draws on “procedural” variables, building on insights from the contentious politics literature. We show that a procedural model outperforms more inductive, atheoretical alternatives and also outperforms models based on countries’ structural characteristics, which previously dominated models of civil war onset. We find that process can substitute for structure over short forecasting windows. We also find a more direct connection between theory and forecasting than is sometimes assumed, though we suggest that future researchers treat the value-added of theory for prediction not as an assumption but rather as a hypothesis to test.

中文翻译：

---

内战预测：“大数据”和机器学习时代的理论和结构

理论是否有助于预测准确性？我们使用事件数据表明，建立在突出的冲突升级理论基础上的简约模型可以比以往更准确，更短的时间范围内预测内战的爆发。我们的预测模型基于有争议的政治文献中的见解，利用了“过程”变量。我们表明，程序模型的表现优于归纳，理论上的替代方案，并且也优于基于国家结构特征的模型，这些模型以前在内战爆发模型中占主导地位。我们发现该过程可以在较短的预测窗口内代替结构。我们还发现理论与预测之间的联系比有时所假设的更为直接，